# Learning to Drive Anywhere with Model-Based Reannotation

arXiv: https://arxiv.org/abs/2505.05592 · HTML: https://mlsanity.com/paper/2505.05592/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 53 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos](https://mlsanity.com/paper/2206.11795/) (2022)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https://mlsanity.com/paper/2412.12392/) (2024)
- [Deep Patch Visual Odometry](https://mlsanity.com/paper/2208.04726/) (2022)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Rapid Exploration for Open-World Navigation with Latent Goal Models](https://mlsanity.com/paper/2104.05859/) (2021)
- [Zero-Shot Visual Imitation](https://mlsanity.com/paper/1804.08606/) (2018)
- [Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation](https://mlsanity.com/paper/1709.10489/) (2017)
- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints](https://mlsanity.com/paper/2202.11271/) (2022)
- [Deep Visual MPC-Policy Learning for Navigation](https://mlsanity.com/paper/1903.02749/) (2019)
- [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022)
- [CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos](https://mlsanity.com/paper/2411.17820/) (2024)
- [Scaling Local Control to Large-Scale Topological Navigation](https://mlsanity.com/paper/1909.12329/) (2019)
- [LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Videos](https://mlsanity.com/paper/2410.03603/) (2024)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation](https://mlsanity.com/paper/2210.07450/) (2022)

## Built on by (most cited first, 13 of 13)

- [OmniVLA: An Omni-Modal Vision-Language-Action Model for Robot Navigation](https://mlsanity.com/paper/2509.19480/) (2025)
- [From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning](https://mlsanity.com/paper/2507.22028/) (2025)
- [SocialNav: Training Human-Inspired Foundation Model for Socially-Aware Embodied Navigation](https://mlsanity.com/paper/2511.21135/) (2025)
- [AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge](https://mlsanity.com/paper/2602.13476/) (2026)
- [UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos](https://mlsanity.com/paper/2510.15018/) (2025)
- [MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning](https://mlsanity.com/paper/2510.03142/) (2025)
- [Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI](https://mlsanity.com/paper/2511.20620/) (2025)
- [Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion](https://mlsanity.com/paper/2603.22527/) (2026)
- [From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation](https://mlsanity.com/paper/2606.12603/) (2026)
- [EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness](https://mlsanity.com/paper/2607.19880/) (2026)
- [CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting](https://mlsanity.com/paper/2608.06688/) (2026)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)
- [Training Generalist Navigation Policies via Action Self-Distillation](https://mlsanity.com/paper/2610.17593/) (2026)

## Abstract (arXiv)

Developing broadly generalizable visual navigation policies for robots is a significant challenge, primarily constrained by the availability of large-scale, diverse training data. While curated datasets collected by researchers offer high quality, their limited size restricts policy generalization. To overcome this, we explore leveraging abundant, passively collected data sources, including large volumes of crowd-sourced teleoperation data and unlabeled YouTube videos, despite their potential for lower quality or missing action labels. We propose Model-Based ReAnnotation (MBRA), a framework that utilizes a learned short-horizon, model-based expert model to relabel or generate high-quality actions for these passive datasets. This relabeled data is then distilled into LogoNav, a long-horizon navigation policy conditioned on visual goals or GPS waypoints. We demonstrate that LogoNav, trained using MBRA-processed data, achieves state-of-the-art performance, enabling robust navigation over distances exceeding 300 meters in previously unseen indoor and outdoor environments. Our extensive real-world evaluations, conducted across a fleet of robots (including quadrupeds) in six cities on three continents, validate the policy's ability to generalize and navigate effectively even amidst pedestrians in crowded settings.